Reduced rank modeling for functional regression with functional responses
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 205-217 |
Journal / Publication | Journal of Multivariate Analysis |
Volume | 169 |
Online published | 18 Sept 2018 |
Publication status | Published - Jan 2019 |
Link(s)
Abstract
This article considers regression problems where both the predictor and the response are functional in nature. Driven by the desire to build a parsimonious model, we consider functional reduced rank regression in the framework of reproducing kernel Hilbert spaces, which can be formulated in the form of linear factor regression with estimated multivariate factors, and achieves dimension reduction in both the predictor and the response spaces. The convergence rate of the estimator is derived. Simulations and real datasets are used to demonstrate the competitive performance of the proposed method.
Research Area(s)
- Dimension reduction, Functional data, Functional response, Reproducing kernel Hilbert space
Citation Format(s)
Reduced rank modeling for functional regression with functional responses. / Lin, Hongmei; Jiang, Xuejun; Lian, Heng et al.
In: Journal of Multivariate Analysis, Vol. 169, 01.2019, p. 205-217.
In: Journal of Multivariate Analysis, Vol. 169, 01.2019, p. 205-217.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review